國立虎尾科技大學 |

Feature Learning and Understanding = Algorithms and Applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Feature Learning and Understanding/ by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang.
其他題名:	Algorithms and Applications /
作者:	Zhao, Haitao.
其他作者:	Zhang, Xianyi.
面頁冊數:	XIV, 291 p. 126 illus., 109 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Image Processing and Computer Vision. -
電子資源:	https://doi.org/10.1007/978-3-030-40794-0
ISBN:	9783030407940

Feature Learning and Understanding = Algorithms and Applications /
Zhao, Haitao.

Feature Learning and UnderstandingAlgorithms and Applications /[electronic resource] :by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang. - 1st ed. 2020. - XIV, 291 p. 126 illus., 109 illus. in color.online resource. - Information Fusion and Data Science,2510-1528. - Information Fusion and Data Science,.

Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

ISBN: 9783030407940

Standard No.: 10.1007/978-3-030-40794-0doiSubjects--Topical Terms:

670819
Image Processing and Computer Vision.

LC Class. No.: QC1-999

Dewey Class. No.: 621

Feature Learning and Understanding = Algorithms and Applications /
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